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In the Mixup training paradigm, a model is trained using convex combinations of data points and their associated labels. Despite seeing very few true data points during training, models trained using Mixup seem to still minimize the original empirical risk and exhibit better generalization and robustness on various tasks when compared to standard training. In this paper, we investigate how these benefits of Mixup training rely on properties of the data in the context of classification. For minimizing the original empirical risk, we compute a closed form for the Mixup-optimal classification, which allows us to construct a simple dataset onmore »Free, publicly-accessible full text available July 1, 2023
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Free, publicly-accessible full text available January 1, 2023
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Despite providing convenience and reducing the travel burden of patients, Video-Conferencing (VC) clinical visits have not enjoyed wide uptake by patients and care providers. It is desired that the medical problems addressed by VC visits can match a face-to-face encounter in scope and quality. Subsequently, VC visits with nurse assistance are emerging; however, the scalable and financially sustainable of such services are unclear. Therefore, we explore the implementability of VC visits with nursing services using a game-theoretic model, and investigate the impact of different pricing schemes (discriminative pricing based on patient characteristics vs. non-discriminative) on patients’ care choices between VCmore »Free, publicly-accessible full text available October 28, 2022
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Free, publicly-accessible full text available November 19, 2022
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Free, publicly-accessible full text available December 16, 2022
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Abstract The implementation of nano-engineered composite oxides opens up the way towards the development of a novel class of functional materials with enhanced electrochemical properties. Here we report on the realization of vertically aligned nanocomposites of lanthanum strontium manganite and doped ceria with straight applicability as functional layers in high-temperature energy conversion devices. By a detailed analysis using complementary state-of-the-art techniques, which include atom-probe tomography combined with oxygen isotopic exchange, we assess the local structural and electrochemical functionalities and we allow direct observation of local fast oxygen diffusion pathways. The resulting ordered mesostructure, which is characterized by a coherent, densemore »Free, publicly-accessible full text available December 1, 2022
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Free, publicly-accessible full text available September 1, 2022
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Protein transfer into nanoscale compartments is critical for many cellular/life processes, yet there are few reports on how compartment properties impact the protein orientation during a transfer. Such a knowledge gap limits a deeper understanding of the protein transfer mechanism, which could be bridged using nanoporous materials. Here, we use a mesoporous silica, a covalent organic framework, and a metal-organic framework with charged, hydrophobic, and neutral surfaces, respectively, to elucidate the impact of channel properties on the transfer of a model protein, lysozyme. Using site-directed spin labeling and time-resolved electron paramagnetic resonance spectroscopy, we reveal that the transfer can bemore »Free, publicly-accessible full text available September 22, 2022